Best performing approach. Table REF  reports the performance of the evaluated methods using standard metrics (precision, recall, and F1-score) on the five considered datasets. For a more intuitive visualization of the results, Fig. REF  shows the F1-score. In general, MTAD-GAT [1]} is the best-performing approach, surpassing other methods on four datasets, namely, SWaT, MSL, SMAP, and UCR. This can be explained by the fact that this approach is hybrid as it is based on forecasting and reconstruction losses. Indeed, this allows the simultaneous detection of local and global anomalies. Nevertheless, it can be remarked that the results obtained on TODS contradict this statement. Indeed, MTAD-GAT registers inferior performance on this benchmark as compared to other methods, including DL and conventional methods. Two hypotheses might justify this drop: (1) the TODS dataset encloses complex anomalies that are moderately local and are hardly captured by a simple forecasting and/or reconstruction approach, favoring probabilistic modeling as in DAGMM [2]}. However, the higher performance obtained for GDN [3]} and USAD [4]} partly disprove this assumption; and (2) the synthetic data in TODS are not realistic, making them hardly predictable.
Another observation that can be made is that GDN [3]} presents the second-best performance on three datasets, namely MSL, SMAP, and UCR. This confirms the relevance of using graph representations for modeling time-series. Surprisingly, graph-based approaches (GDN and MTAD-GAT) remain relatively effective on a univariate dataset (UCR), although modeling the connectivity between variables is unnecessary.
